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Record W1984318668 · doi:10.1088/0960-1317/18/8/085004

Lithographic stress control for the self-assembly of polymer MEMS structures

2008· article· en· W1984318668 on OpenAlexaff
S.-W. Lee, Dan Sameoto, Alireza Mahanfar, M. Parameswaran

Bibliographic record

VenueJournal of Micromechanics and Microengineering · 2008
Typearticle
Languageen
FieldEngineering
TopicAdvanced MEMS and NEMS Technologies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMicroelectromechanical systemsLithographyStress (linguistics)Materials sciencePolymerNanotechnologyEngineeringMechanical engineeringOptoelectronicsComposite material

Abstract

fetched live from OpenAlex

We present a novel self-assembly mechanism to produce an assortment of predetermined three-dimensional micromechanical structures in polymer MEMS technology using lithographically defined areas of stress and mechanical reinforcement within a single structural material. This self-assembly technology is based on the tensile stress that arises during the cross-linking of the negative tone, epoxy-based photoresist SU-8. Two different thicknesses of SU-8 are used in a single compliant structure. The first SU-8 layer forms the main structural element and the second SU-8 layer determines the aspects of self-assembly. The second SU-8 layer thickness acts to both to create a stress differential within the structure as well as define the direction in which the induced stress will cause the structure to deform. In this manner, both the magnitude and direction of self-assembled structures can be controlled using a single lithographic step. Although this technique uses a single structural material, the basic concept may be adapted for other processes, with different material choices, for a wide variety of applications.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score0.464

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.188
Teacher spread0.182 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations13
Published2008
Admission routes1
Has abstractyes

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